In the last few chapters, we covered supervised learning methods, where the training data is labeled with the true outcome that we would like to predict (for example, a rating for recommendations and class assignment for classification or a real target variable in the case of regression).
Next, we will consider the case where we do not have labeled data available. This is called unsupervised learning, as the model is not supervised with the true target label. The unsupervised case is very common in practice, since obtaining labeled training data can be very difficult or expensive in many real-world scenarios (for example, having humans label training data with class labels for classification). However, we would still like to learn some underlying structure in the data and use these to make predictions.
This is where unsupervised learning approaches can be useful. Unsupervised...